It is crucial for autonomous agents to plan for complex tasks in everyday settings from visual observations [1]}. Although reinforcement learning provides a powerful and general framework for decision making and control, its application in practice is often hindered by the need for extensive feature and reward engineering [2]}. Moreover, deep RL algorithms are often sensitive to factors such as reward sparsity and magnitude, making well-performing reward functions particularly difficult to engineer. In many real-world applications, specifying a proper reward function is difficult.
